Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Monitoring the composition and form of urban environments based on the vegetation-impervious surface-soil (VIS) model by sub-pixel analysis techniques

The majority of the world's population now resides in urban environments and information on the internal composition and dynamics of these environments is essential to enable preservation of certain standards of living. Remotely sensed data, especially the global coverage of moderate spatial resolution satellites such as Landsat, Indian Resource Satellite and Systeme Pour I'Observation de la Terre (SPOT), offer a highly useful data source for mapping the composition of these cities and examining their changes over time. The utility and range of applications for remotely sensed data in urban environments could be improved with a more appropriate conceptual model relating urban environments to the sampling resolutions of imaging sensors and processing routines. Hence, the aim of this work was to take the Vegetation-Impervious surface-Soil (VIS) model of urban composition and match it with the most appropriate image processing methodology to deliver information on VIS composition for urban environments. Several approaches were evaluated for mapping the urban composition of Brisbane city (south-cast Queensland, Australia) using Landsat 5 Thematic Mapper data and 1:5000 aerial photographs. The methods evaluated were: image classification; interpretation of aerial photographs; and constrained linear mixture analysis. Over 900 reference sample points on four transects were extracted from the aerial photographs and used as a basis to check output of the classification and mixture analysis. Distinctive zonations of VIS related to urban composition were found in the per-pixel classification and aggregated air-photo interpretation; however, significant spectral confusion also resulted between classes. In contrast, the VIS fraction images produced from the mixture analysis enabled distinctive densities of commercial, industrial and residential zones within the city to be clearly defined, based on their relative amount of vegetation cover. The soil fraction image served as an index for areas being (re)developed. The logical match of a low (L)-resolution, spectral mixture analysis approach with the moderate spatial resolution image data, ensured the processing model matched the spectrally heterogeneous nature of the urban environments at the scale of Landsat Thematic Mapper data.

Enriched behavioral prediction equation and its impact on structured learning and the dynamic calculus

This theoretical note describes an expansion of the behavioral prediction equation, in line with the greater complexity encountered in models of structured learning theory (R. B. Cattell, 1996a). This presents learning theory with a vector substitute for the simpler scalar quantities by which traditional Pavlovian-Skinnerian models have hitherto been represented. Structured learning can be demonstrated by vector changes across a range of intrapersonal psychological variables (ability, personality, motivation, and state constructs). Its use with motivational dynamic trait measures (R. B. Cattell, 1985) should reveal new theoretical possibilities for scientifically monitoring change processes (dynamic calculus model; R. B. Cattell, 1996b), such as encountered within psycho therapeutic settings (R. B. Cattell, 1987). The enhanced behavioral prediction equation suggests that static conceptualizations of personality structure such as the Big Five model are less than optimal.

Monitoring and isolation of blood dendritic cells from apheresis products in healthy individuals: a platform for cancer immunotherapy

The fundamental role of dendritic cells (DC in initiating and directing the primary immune response is well established. Furthermore, it is now accepted that DC may be useful in new vaccination strategies for preventing certain malignant and infectious diseases. As blood DC (BDC physiology differs from that of the DC homologues generated in vitro from monocyte precursors, it is becoming more relevant to consider BDC for therapeutic interventions. Until recently, protocols for the isolation of BDC were laborious and inefficient; therefore, their use for investigative cancer immunotherapy is not widespread. In this study, we carefully documented BDC counts, yields and subsets during apheresis (Cobe Spectra), the initial and essential procedure in creating a BDC isolation platform for cancer immunotherapy. We established that an automated software package (Version 6,0 AutoPBPC) provides an operator-independent reliable source of motionuclear cells (MNC for BDC preparation. Further, we observed that BDC might be recovered in high yields, often greater than 100% relative to the number of circulating BDC predicted by blood volume. An average of 66 million (range, 17-179) BDC per 10-1 procedure were obtained, largely satisfying the needs for immunization. Higher yields were possible on total processed blood volumes of 151. BDC were not activated by the isolation procedure and, more importantly, both BDC subsets (CD11c(+)CD123(low) and CD11c(-)CD123(high)) were equally represented. Finally, we established that the apheresis product could be used for antibody-based BDC immunoselection and demonstrated that fully functional BDC can be obtained by this procedure. (C) 2002 Published by Elsevier Science B.V.

The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.

Warranty and optimal reliability improvement through product development

For products sold with warranty, the warranty servicing cost can be reduced by improving product reliability through a development process. However, this increases the unit manufacturing cost. Optimal development must achieve a trade-off between these two costs. The outcome of the development process is uncertain and needs to be taken into account in the determination of the optimal development effort. The paper develops a model where this uncertainty is taken into account. (C) 2003 Elsevier Ltd. All rights reserved.

New fluorescence parameters for monitoring photosynthesis in plants - 1. The effect of illumination on the fluorescence parameters of the JIP-test

Chlorophyll fluorescence measurements have a wide range of applications from basic understanding of photosynthesis functioning to plant environmental stress responses and direct assessments of plant health. The measured signal is the fluorescence intensity (expressed in relative units) and the most meaningful data are derived from the time dependent increase in fluorescence intensity achieved upon application of continuous bright light to a previously dark adapted sample. The fluorescence response changes over time and is termed the Kautsky curve or chlorophyll fluorescence transient. Recently, Strasser and Strasser (1995) formulated a group of fluorescence parameters, called the JIP-test, that quantify the stepwise flow of energy through Photosystem II, using input data from the fluorescence transient. The purpose of this study was to establish relationships between the biochemical reactions occurring in PS II and specific JIP-test parameters. This was approached using isolated systems that facilitated the addition of modifying agents, a PS II electron transport inhibitor, an electron acceptor and an uncoupler, whose effects on PS II activity are well documented in the literature. The alteration to PS II activity caused by each of these compounds could then be monitored through the JIP-test parameters and compared and contrasted with the literature. The known alteration in PS II activity of Chenopodium album atrazine resistant and sensitive biotypes was also used to gauge the effectiveness and sensitivity of the JIP-test. The information gained from the in vitro study was successfully applied to an in situ study. This is the first in a series of four papers. It shows that the trapping parameters of the JIP-test were most affected by illumination and that the reduction in trapping had a run-on effect to inhibit electron transport. When irradiance exposure proceeded to photoinhibition, the electron transport probability parameter was greatly reduced and dissipation significantly increased. These results illustrate the advantage of monitoring a number of fluorescence parameters over the use of just one, which is often the case when the F-V/F-M ratio is used.

Balancing the number and size of sites: An economic approach to the optimal design of cluster samples

The design of randomized controlled trials entails decisions that have economic as well as statistical implications. In particular, the choice of an individual or cluster randomization design may affect the cost of achieving the desired level of power, other things being equal. Furthermore, if cluster randomization is chosen, the researcher must decide how to balance the number of clusters, or sites, and the size of each site. This article investigates these interrelated statistical and economic issues. Its principal purpose is to elucidate the statistical and economic trade-offs to assist researchers to employ randomized controlled trials that have desired economic, as well as statistical, properties. (C) 2003 Elsevier Inc. All rights reserved.